What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Artificial intelligence, Machine learning, Data mining, Dynamic time warping and Class.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Animal ecology and Time series.
In the subject of general Machine learning, his work in Class imbalance and Feature is often linked to Simple, Treatment method and Cost curve, thereby combining diverse domains of study.
His Data mining research is multidisciplinary, relying on both Receiver operating characteristic, Missing data, Imputation and Cluster analysis.
Gustavo E. A. P. A. Batista interconnects Data stream mining, Pairwise comparison, Bottleneck, Nearest neighbor search and Search algorithm in the investigation of issues within Dynamic time warping.
In his research on the topic of Class, Decision tree is strongly related with Training set.
His most cited work include:
- A study of the behavior of several methods for balancing machine learning training data (1795 citations)
- Searching and mining trillions of time series subsequences under dynamic time warping (626 citations)
- An analysis of four missing data treatment methods for supervised learning (500 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Machine learning, Data mining, Pattern recognition and Classifier are his primary areas of study.
The Artificial intelligence study combines topics in areas such as Class, Data stream and Time series.
His research in Machine learning intersects with topics in Bottleneck and Training set.
Gustavo E. A. P. A. Batista has included themes like Ranking, Time series classification and Missing data, Imputation in his Data mining study.
His Pattern recognition research incorporates elements of Time domain and Representation.
The various areas that Gustavo E. A. P. A. Batista examines in his Classifier study include Classifier, Area under the roc curve and Word error rate.
He most often published in these fields:
- Artificial intelligence (64.23%)
- Machine learning (46.34%)
- Data mining (34.15%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (64.23%)
- Data stream mining (13.82%)
- Data mining (34.15%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Data stream mining, Data mining, Algorithm and Pattern recognition.
His Artificial intelligence study incorporates themes from Data stream and Machine learning, Time series.
His study ties his expertise on Class together with the subject of Machine learning.
His work on Concept drift as part of general Data mining research is frequently linked to Simple, Sample mean and sample covariance, Statistical difference and Quantification methods, thereby connecting diverse disciplines of science.
Within one scientific family, Gustavo E. A. P. A. Batista focuses on topics pertaining to Tree under Pattern recognition, and may sometimes address concerns connected to Data point and Training set.
Gustavo E. A. P. A. Batista focuses mostly in the field of Dynamic time warping, narrowing it down to topics relating to Image warping and, in certain cases, Pruning, Distance measures, Range and Nearest neighbor search.
Between 2015 and 2021, his most popular works were:
- Speeding up all-pairwise dynamic time warping matrix calculation (69 citations)
- Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test (52 citations)
- Speeding up similarity search under dynamic time warping by pruning unpromising alignments (34 citations)
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